Overview

Dataset statistics

Number of variables20
Number of observations26869
Missing cells39697
Missing cells (%)7.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 MiB
Average record size in memory157.0 B

Variable types

Categorical6
Numeric13
Boolean1

Warnings

convert_flag is highly correlated with derm_ratioHigh correlation
tbm_present_flag is highly correlated with idsr_present_flag and 4 other fieldsHigh correlation
sam_present_flag is highly correlated with narc_segment and 1 other fieldsHigh correlation
idsr_present_flag is highly correlated with tbm_present_flag and 1 other fieldsHigh correlation
narc_segment is highly correlated with sam_present_flagHigh correlation
is_urban is highly correlated with tbm_present_flagHigh correlation
Cytopoint_calls is highly correlated with Apoquel_callsHigh correlation
Apoquel_calls is highly correlated with Cytopoint_callsHigh correlation
derm_ratio is highly correlated with convert_flag and 2 other fieldsHigh correlation
last12monthsproductuse is highly correlated with tbm_present_flag and 2 other fieldsHigh correlation
total_idsr_calls_past_6months is highly correlated with idsr_present_flagHigh correlation
total_sam_calls_past_6months is highly correlated with sam_present_flagHigh correlation
zts_market_share is highly correlated with tbm_present_flag and 1 other fieldsHigh correlation
convert_flag is highly correlated with derm_ratioHigh correlation
tbm_present_flag is highly correlated with idsr_present_flag and 6 other fieldsHigh correlation
sam_present_flag is highly correlated with total_sam_calls_past_6monthsHigh correlation
idsr_present_flag is highly correlated with tbm_present_flag and 2 other fieldsHigh correlation
narc_segment is highly correlated with last12monthstotalsalesHigh correlation
is_urban is highly correlated with tbm_present_flagHigh correlation
calls_ratio is highly correlated with Cytopoint_callsHigh correlation
Cytopoint_calls is highly correlated with calls_ratio and 1 other fieldsHigh correlation
Apoquel_calls is highly correlated with Cytopoint_callsHigh correlation
derm_ratio is highly correlated with convert_flag and 2 other fieldsHigh correlation
last12monthstotalsales is highly correlated with tbm_present_flag and 4 other fieldsHigh correlation
last12monthsproductuse is highly correlated with tbm_present_flag and 2 other fieldsHigh correlation
total_idsr_calls_past_6months is highly correlated with idsr_present_flag and 1 other fieldsHigh correlation
total_tbm_calls_past_6months is highly correlated with tbm_present_flagHigh correlation
total_sam_calls_past_6months is highly correlated with sam_present_flagHigh correlation
zts_market_share is highly correlated with tbm_present_flagHigh correlation
convert_flag is highly correlated with derm_ratioHigh correlation
tbm_present_flag is highly correlated with idsr_present_flag and 5 other fieldsHigh correlation
sam_present_flag is highly correlated with total_sam_calls_past_6monthsHigh correlation
idsr_present_flag is highly correlated with tbm_present_flag and 2 other fieldsHigh correlation
is_urban is highly correlated with tbm_present_flagHigh correlation
calls_ratio is highly correlated with Cytopoint_callsHigh correlation
Cytopoint_calls is highly correlated with calls_ratioHigh correlation
derm_ratio is highly correlated with convert_flag and 1 other fieldsHigh correlation
last12monthstotalsales is highly correlated with tbm_present_flag and 2 other fieldsHigh correlation
last12monthsproductuse is highly correlated with tbm_present_flag and 1 other fieldsHigh correlation
total_idsr_calls_past_6months is highly correlated with idsr_present_flagHigh correlation
total_sam_calls_past_6months is highly correlated with sam_present_flagHigh correlation
zts_market_share is highly correlated with tbm_present_flagHigh correlation
convert_flag is highly correlated with tbm_present_flag and 3 other fieldsHigh correlation
total_idsr_calls_past_6months is highly correlated with idsr_present_flagHigh correlation
tbm_present_flag is highly correlated with convert_flag and 5 other fieldsHigh correlation
sam_present_flag is highly correlated with narc_segmentHigh correlation
zts_market_share is highly correlated with tbm_present_flag and 3 other fieldsHigh correlation
idsr_present_flag is highly correlated with total_idsr_calls_past_6months and 2 other fieldsHigh correlation
is_urban is highly correlated with convert_flag and 2 other fieldsHigh correlation
narc_segment is highly correlated with sam_present_flag and 1 other fieldsHigh correlation
Cytopoint_calls is highly correlated with Apoquel_callsHigh correlation
last12monthsproductuse is highly correlated with convert_flag and 5 other fieldsHigh correlation
corp_flag is highly correlated with tbm_present_flag and 1 other fieldsHigh correlation
Apoquel_calls is highly correlated with Cytopoint_callsHigh correlation
petcareregion is highly correlated with zts_market_shareHigh correlation
derm_ratio is highly correlated with convert_flagHigh correlation
tbm_present_flag is highly correlated with idsr_present_flag and 2 other fieldsHigh correlation
idsr_present_flag is highly correlated with tbm_present_flagHigh correlation
petcareregion is highly correlated with tbm_present_flagHigh correlation
is_urban is highly correlated with tbm_present_flagHigh correlation
petcareregion has 4885 (18.2%) missing values Missing
narc_segment has 5961 (22.2%) missing values Missing
calls_ratio has 9617 (35.8%) missing values Missing
Cytopoint_calls has 9617 (35.8%) missing values Missing
Apoquel_calls has 9617 (35.8%) missing values Missing
calls_ratio has 4369 (16.3%) zeros Zeros
Cytopoint_calls has 4369 (16.3%) zeros Zeros
Apoquel_calls has 691 (2.6%) zeros Zeros
derm_ratio has 5893 (21.9%) zeros Zeros
total_idsr_calls_past_6months has 14511 (54.0%) zeros Zeros
total_tbm_calls_past_6months has 9090 (33.8%) zeros Zeros
total_sam_calls_past_6months has 24878 (92.6%) zeros Zeros
zts_market_share has 6876 (25.6%) zeros Zeros

Reproduction

Analysis started2021-10-14 13:05:39.302090
Analysis finished2021-10-14 13:06:26.763463
Duration47.46 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

convert_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size419.8 KiB
0
13453 
1
13416 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26869
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
013453
50.1%
113416
49.9%

Length

2021-10-14T09:06:27.338007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T09:06:27.436403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
013453
50.1%
113416
49.9%

Most occurring characters

ValueCountFrequency (%)
013453
50.1%
113416
49.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26869
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013453
50.1%
113416
49.9%

Most occurring scripts

ValueCountFrequency (%)
Common26869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013453
50.1%
113416
49.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII26869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013453
50.1%
113416
49.9%

petcareregion
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing4885
Missing (%)18.2%
Memory size419.8 KiB
MIDWEST (Region)
4686 
SOUTHEAST (Region)
4523 
CENTRAL (Region)
4294 
NORTHEAST (Region)
4255 
WEST (Region)
4226 

Length

Max length18
Median length16
Mean length16.22188865
Min length13

Characters and Unicode

Total characters356622
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNORTHEAST (Region)
2nd rowWEST (Region)
3rd rowCENTRAL (Region)
4th rowNORTHEAST (Region)
5th rowSOUTHEAST (Region)

Common Values

ValueCountFrequency (%)
MIDWEST (Region)4686
17.4%
SOUTHEAST (Region)4523
16.8%
CENTRAL (Region)4294
16.0%
NORTHEAST (Region)4255
15.8%
WEST (Region)4226
15.7%
(Missing)4885
18.2%

Length

2021-10-14T09:06:27.697864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T09:06:27.798596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
region21984
50.0%
midwest4686
 
10.7%
southeast4523
 
10.3%
central4294
 
9.8%
northeast4255
 
9.7%
west4226
 
9.6%

Most occurring characters

ValueCountFrequency (%)
T30762
 
8.6%
R30533
 
8.6%
S22213
 
6.2%
E21984
 
6.2%
21984
 
6.2%
(21984
 
6.2%
e21984
 
6.2%
g21984
 
6.2%
i21984
 
6.2%
o21984
 
6.2%
Other values (13)119226
33.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter180750
50.7%
Lowercase Letter109920
30.8%
Space Separator21984
 
6.2%
Open Punctuation21984
 
6.2%
Close Punctuation21984
 
6.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T30762
17.0%
R30533
16.9%
S22213
12.3%
E21984
12.2%
A13072
7.2%
W8912
 
4.9%
O8778
 
4.9%
H8778
 
4.9%
N8549
 
4.7%
M4686
 
2.6%
Other values (5)22483
12.4%
Lowercase Letter
ValueCountFrequency (%)
e21984
20.0%
g21984
20.0%
i21984
20.0%
o21984
20.0%
n21984
20.0%
Space Separator
ValueCountFrequency (%)
21984
100.0%
Open Punctuation
ValueCountFrequency (%)
(21984
100.0%
Close Punctuation
ValueCountFrequency (%)
)21984
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin290670
81.5%
Common65952
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
T30762
10.6%
R30533
10.5%
S22213
 
7.6%
E21984
 
7.6%
e21984
 
7.6%
g21984
 
7.6%
i21984
 
7.6%
o21984
 
7.6%
n21984
 
7.6%
A13072
 
4.5%
Other values (10)62186
21.4%
Common
ValueCountFrequency (%)
21984
33.3%
(21984
33.3%
)21984
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII356622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T30762
 
8.6%
R30533
 
8.6%
S22213
 
6.2%
E21984
 
6.2%
21984
 
6.2%
(21984
 
6.2%
e21984
 
6.2%
g21984
 
6.2%
i21984
 
6.2%
o21984
 
6.2%
Other values (13)119226
33.4%

corp_flag
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size419.8 KiB
0
16210 
1
10659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26869
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
016210
60.3%
110659
39.7%

Length

2021-10-14T09:06:28.110760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T09:06:28.205511image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
016210
60.3%
110659
39.7%

Most occurring characters

ValueCountFrequency (%)
016210
60.3%
110659
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26869
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016210
60.3%
110659
39.7%

Most occurring scripts

ValueCountFrequency (%)
Common26869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016210
60.3%
110659
39.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII26869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016210
60.3%
110659
39.7%

tbm_present_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size419.8 KiB
1
21984 
0
4885 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26869
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
121984
81.8%
04885
 
18.2%

Length

2021-10-14T09:06:28.470798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T09:06:28.563548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
121984
81.8%
04885
 
18.2%

Most occurring characters

ValueCountFrequency (%)
121984
81.8%
04885
 
18.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26869
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
121984
81.8%
04885
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
Common26869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
121984
81.8%
04885
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII26869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121984
81.8%
04885
 
18.2%

sam_present_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size419.8 KiB
0
24928 
1
 
1941

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26869
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
024928
92.8%
11941
 
7.2%

Length

2021-10-14T09:06:28.802907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T09:06:28.894690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
024928
92.8%
11941
 
7.2%

Most occurring characters

ValueCountFrequency (%)
024928
92.8%
11941
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26869
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
024928
92.8%
11941
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Common26869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
024928
92.8%
11941
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII26869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
024928
92.8%
11941
 
7.2%

idsr_present_flag
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size419.8 KiB
1
14316 
0
12553 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters26869
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
114316
53.3%
012553
46.7%

Length

2021-10-14T09:06:29.157959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-14T09:06:29.249747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
114316
53.3%
012553
46.7%

Most occurring characters

ValueCountFrequency (%)
114316
53.3%
012553
46.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26869
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
114316
53.3%
012553
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common26869
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
114316
53.3%
012553
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII26869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
114316
53.3%
012553
46.7%

narc_segment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing5961
Missing (%)22.2%
Infinite0
Infinite (%)0.0%
Mean3.13090683
Minimum0
Maximum5
Zeros127
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:29.328502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.014665221
Coefficient of variation (CV)0.3240802989
Kurtosis-0.4059675456
Mean3.13090683
Median Absolute Deviation (MAD)1
Skewness-0.05481643022
Sum65461
Variance1.029545511
MonotonicityNot monotonic
2021-10-14T09:06:29.454202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
37052
26.2%
45986
22.3%
25406
20.1%
51803
 
6.7%
1534
 
2.0%
0127
 
0.5%
(Missing)5961
22.2%
ValueCountFrequency (%)
0127
 
0.5%
1534
 
2.0%
25406
20.1%
37052
26.2%
45986
22.3%
51803
 
6.7%
ValueCountFrequency (%)
51803
 
6.7%
45986
22.3%
37052
26.2%
25406
20.1%
1534
 
2.0%
0127
 
0.5%

is_urban
Boolean

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size236.2 KiB
True
18115 
False
8754 
ValueCountFrequency (%)
True18115
67.4%
False8754
32.6%
2021-10-14T09:06:29.555895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

calls_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct177
Distinct (%)1.0%
Missing9617
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean0.337991525
Minimum0
Maximum1
Zeros4369
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:29.676600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.3636363636
Q30.5
95-th percentile0.7142857143
Maximum1
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2508936651
Coefficient of variation (CV)0.7423075627
Kurtosis0.0502829758
Mean0.337991525
Median Absolute Deviation (MAD)0.1363636364
Skewness0.3268417676
Sum5831.029789
Variance0.06294763119
MonotonicityNot monotonic
2021-10-14T09:06:29.852135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04369
16.3%
0.53977
14.8%
0.33333333331914
 
7.1%
0.4950
 
3.5%
0.25935
 
3.5%
1691
 
2.6%
0.4285714286477
 
1.8%
0.6666666667445
 
1.7%
0.2413
 
1.5%
0.6283
 
1.1%
Other values (167)2798
 
10.4%
(Missing)9617
35.8%
ValueCountFrequency (%)
04369
16.3%
0.041
 
< 0.1%
0.055555555561
 
< 0.1%
0.06252
 
< 0.1%
0.066666666672
 
< 0.1%
0.071428571432
 
< 0.1%
0.076923076921
 
< 0.1%
0.083333333334
 
< 0.1%
0.090909090915
 
< 0.1%
0.18
 
< 0.1%
ValueCountFrequency (%)
1691
2.6%
0.95833333331
 
< 0.1%
0.91666666671
 
< 0.1%
0.91
 
< 0.1%
0.88888888891
 
< 0.1%
0.8752
 
< 0.1%
0.85714285713
 
< 0.1%
0.84210526321
 
< 0.1%
0.833333333314
 
0.1%
0.829
 
0.1%

Cytopoint_calls
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct41
Distinct (%)0.2%
Missing9617
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean2.02254811
Minimum0
Maximum42
Zeros4369
Zeros (%)16.3%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:30.021648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum42
Range42
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.764166024
Coefficient of variation (CV)1.366675042
Kurtosis42.30719357
Mean2.02254811
Median Absolute Deviation (MAD)1
Skewness4.927358211
Sum34893
Variance7.640613807
MonotonicityNot monotonic
2021-10-14T09:06:30.174268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
15143
19.1%
04369
16.3%
23099
 
11.5%
31788
 
6.7%
41040
 
3.9%
5668
 
2.5%
6387
 
1.4%
7220
 
0.8%
8132
 
0.5%
9103
 
0.4%
Other values (31)303
 
1.1%
(Missing)9617
35.8%
ValueCountFrequency (%)
04369
16.3%
15143
19.1%
23099
11.5%
31788
 
6.7%
41040
 
3.9%
5668
 
2.5%
6387
 
1.4%
7220
 
0.8%
8132
 
0.5%
9103
 
0.4%
ValueCountFrequency (%)
421
 
< 0.1%
411
 
< 0.1%
402
< 0.1%
382
< 0.1%
371
 
< 0.1%
363
< 0.1%
351
 
< 0.1%
342
< 0.1%
333
< 0.1%
311
 
< 0.1%

Apoquel_calls
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct47
Distinct (%)0.3%
Missing9617
Missing (%)35.8%
Infinite0
Infinite (%)0.0%
Mean3.192267563
Minimum0
Maximum52
Zeros691
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:30.339799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile8
Maximum52
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.217946895
Coefficient of variation (CV)1.008044229
Kurtosis36.74492633
Mean3.192267563
Median Absolute Deviation (MAD)1
Skewness4.441369616
Sum55073
Variance10.35518222
MonotonicityNot monotonic
2021-10-14T09:06:30.520347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
14673
17.4%
23636
 
13.5%
32818
 
10.5%
41883
 
7.0%
51193
 
4.4%
6780
 
2.9%
0691
 
2.6%
7456
 
1.7%
8307
 
1.1%
9213
 
0.8%
Other values (37)602
 
2.2%
(Missing)9617
35.8%
ValueCountFrequency (%)
0691
 
2.6%
14673
17.4%
23636
13.5%
32818
10.5%
41883
7.0%
51193
 
4.4%
6780
 
2.9%
7456
 
1.7%
8307
 
1.1%
9213
 
0.8%
ValueCountFrequency (%)
521
 
< 0.1%
511
 
< 0.1%
501
 
< 0.1%
471
 
< 0.1%
451
 
< 0.1%
423
< 0.1%
401
 
< 0.1%
391
 
< 0.1%
383
< 0.1%
372
< 0.1%

derm_ratio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20818
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3295317979
Minimum0
Maximum4.515088424
Zeros5893
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:30.697867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.07418818899
median0.3106364708
Q30.5311209116
95-th percentile0.7919726285
Maximum4.515088424
Range4.515088424
Interquartile range (IQR)0.4569327226

Descriptive statistics

Standard deviation0.2680445303
Coefficient of variation (CV)0.8134102141
Kurtosis1.797381859
Mean0.3295317979
Median Absolute Deviation (MAD)0.2269697129
Skewness0.5570822222
Sum8854.189877
Variance0.0718478702
MonotonicityNot monotonic
2021-10-14T09:06:30.862429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05893
 
21.9%
1158
 
0.6%
0.17529054242
 
< 0.1%
0.54309871532
 
< 0.1%
0.36112111981
 
< 0.1%
0.54208612381
 
< 0.1%
0.48809820781
 
< 0.1%
0.090465890561
 
< 0.1%
0.18684658761
 
< 0.1%
0.81268847761
 
< 0.1%
Other values (20808)20808
77.4%
ValueCountFrequency (%)
05893
21.9%
0.0014578577141
 
< 0.1%
0.0020460159531
 
< 0.1%
0.0030161070971
 
< 0.1%
0.0031761095631
 
< 0.1%
0.0041324099161
 
< 0.1%
0.0044308138341
 
< 0.1%
0.005039332631
 
< 0.1%
0.0054231678531
 
< 0.1%
0.0058702309131
 
< 0.1%
ValueCountFrequency (%)
4.5150884241
 
< 0.1%
2.8656639871
 
< 0.1%
2.6786974651
 
< 0.1%
1.0939571651
 
< 0.1%
1158
0.6%
0.99762024861
 
< 0.1%
0.99708956791
 
< 0.1%
0.99644085091
 
< 0.1%
0.99384540421
 
< 0.1%
0.99335680821
 
< 0.1%

last12monthstotalsales
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct25925
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99283.05405
Minimum0
Maximum8218662.612
Zeros16
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:31.042944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1050.328024
Q132625.40999
median72202.42994
Q3130107.9698
95-th percentile290856.4996
Maximum8218662.612
Range8218662.612
Interquartile range (IQR)97482.55983

Descriptive statistics

Standard deviation128124.7894
Coefficient of variation (CV)1.290500082
Kurtosis729.0068234
Mean99283.05405
Median Absolute Deviation (MAD)46187.42987
Skewness15.81177685
Sum2667636379
Variance1.641596165 × 1010
MonotonicityNot monotonic
2021-10-14T09:06:31.203489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
148.9700012201
 
0.7%
157.880004960
 
0.2%
473.640014658
 
0.2%
315.760009852
 
0.2%
631.520019545
 
0.2%
297.940002441
 
0.2%
306.850006133
 
0.1%
780.490020827
 
0.1%
464.73001127
 
0.1%
622.610015926
 
0.1%
Other values (25915)26299
97.9%
ValueCountFrequency (%)
016
 
0.1%
12.439999581
 
< 0.1%
44.779998781
 
< 0.1%
81.949996951
 
< 0.1%
110.81999971
 
< 0.1%
122.89999771
 
< 0.1%
148.9700012201
0.7%
152.27999881
 
< 0.1%
157.880004960
 
0.2%
160.33000094
 
< 0.1%
ValueCountFrequency (%)
8218662.6121
< 0.1%
4319508.821
< 0.1%
3553855.811
< 0.1%
3191271.2971
< 0.1%
3168940.3091
< 0.1%
3074610.1141
< 0.1%
2988222.6241
< 0.1%
2606218.8951
< 0.1%
2487017.7391
< 0.1%
1974797.3721
< 0.1%

last12monthsproductuse
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct68
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.82816629
Minimum0
Maximum87
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:31.378150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q115
median23
Q330
95-th percentile39
Maximum87
Range87
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.52720346
Coefficient of variation (CV)0.528088494
Kurtosis-0.4909146533
Mean21.82816629
Median Absolute Deviation (MAD)7
Skewness-0.1983633778
Sum586501
Variance132.8764197
MonotonicityNot monotonic
2021-10-14T09:06:31.536409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251128
 
4.2%
231091
 
4.1%
221075
 
4.0%
261062
 
4.0%
11045
 
3.9%
211038
 
3.9%
241036
 
3.9%
271032
 
3.8%
2999
 
3.7%
3936
 
3.5%
Other values (58)16427
61.1%
ValueCountFrequency (%)
011
 
< 0.1%
11045
3.9%
2999
3.7%
3936
3.5%
4746
2.8%
5600
2.2%
6423
1.6%
7184
 
0.7%
8105
 
0.4%
998
 
0.4%
ValueCountFrequency (%)
871
 
< 0.1%
741
 
< 0.1%
681
 
< 0.1%
662
< 0.1%
631
 
< 0.1%
622
< 0.1%
611
 
< 0.1%
603
< 0.1%
591
 
< 0.1%
582
< 0.1%

total_idsr_calls_past_6months
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct62
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.730172318
Minimum0
Maximum77
Zeros14511
Zeros (%)54.0%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:31.704471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile15
Maximum77
Range77
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.863560054
Coefficient of variation (CV)1.571927395
Kurtosis12.89405558
Mean3.730172318
Median Absolute Deviation (MAD)0
Skewness2.691325551
Sum100226
Variance34.38133651
MonotonicityNot monotonic
2021-10-14T09:06:31.883509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014511
54.0%
51178
 
4.4%
41176
 
4.4%
61120
 
4.2%
71018
 
3.8%
31010
 
3.8%
2895
 
3.3%
8881
 
3.3%
9761
 
2.8%
10706
 
2.6%
Other values (52)3613
 
13.4%
ValueCountFrequency (%)
014511
54.0%
1582
 
2.2%
2895
 
3.3%
31010
 
3.8%
41176
 
4.4%
51178
 
4.4%
61120
 
4.2%
71018
 
3.8%
8881
 
3.3%
9761
 
2.8%
ValueCountFrequency (%)
771
 
< 0.1%
741
 
< 0.1%
721
 
< 0.1%
671
 
< 0.1%
661
 
< 0.1%
641
 
< 0.1%
602
 
< 0.1%
551
 
< 0.1%
546
< 0.1%
531
 
< 0.1%

total_tbm_calls_past_6months
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct202
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.744352227
Minimum0
Maximum732
Zeros9090
Zeros (%)33.8%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:32.084973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q312
95-th percentile37
Maximum732
Range732
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.92357347
Coefficient of variation (CV)2.147251349
Kurtosis227.1756338
Mean9.744352227
Median Absolute Deviation (MAD)4
Skewness10.73898966
Sum261821
Variance437.7959266
MonotonicityNot monotonic
2021-10-14T09:06:32.253520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09090
33.8%
11431
 
5.3%
21364
 
5.1%
31265
 
4.7%
41143
 
4.3%
51060
 
3.9%
6992
 
3.7%
7866
 
3.2%
8763
 
2.8%
9738
 
2.7%
Other values (192)8157
30.4%
ValueCountFrequency (%)
09090
33.8%
11431
 
5.3%
21364
 
5.1%
31265
 
4.7%
41143
 
4.3%
51060
 
3.9%
6992
 
3.7%
7866
 
3.2%
8763
 
2.8%
9738
 
2.7%
ValueCountFrequency (%)
7321
< 0.1%
6981
< 0.1%
6171
< 0.1%
6081
< 0.1%
4731
< 0.1%
4701
< 0.1%
4442
< 0.1%
4181
< 0.1%
4121
< 0.1%
3601
< 0.1%

total_sam_calls_past_6months
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct156
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.01153746
Minimum0
Maximum592
Zeros24878
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:32.441046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum592
Range592
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.58682349
Coefficient of variation (CV)6.754447164
Kurtosis445.7167146
Mean2.01153746
Median Absolute Deviation (MAD)0
Skewness16.64234535
Sum54048
Variance184.6017725
MonotonicityNot monotonic
2021-10-14T09:06:32.609569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
024878
92.6%
2143
 
0.5%
1128
 
0.5%
3108
 
0.4%
496
 
0.4%
675
 
0.3%
869
 
0.3%
1064
 
0.2%
961
 
0.2%
758
 
0.2%
Other values (146)1189
 
4.4%
ValueCountFrequency (%)
024878
92.6%
1128
 
0.5%
2143
 
0.5%
3108
 
0.4%
496
 
0.4%
549
 
0.2%
675
 
0.3%
758
 
0.2%
869
 
0.3%
961
 
0.2%
ValueCountFrequency (%)
5921
< 0.1%
5421
< 0.1%
4471
< 0.1%
4101
< 0.1%
3571
< 0.1%
3531
< 0.1%
3491
< 0.1%
3421
< 0.1%
3361
< 0.1%
2761
< 0.1%

median_household_income
Real number (ℝ≥0)

Distinct10052
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60409.18218
Minimum0
Maximum215338
Zeros154
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:32.799619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32979
Q144004
median55255
Q372457
95-th percentile103696
Maximum215338
Range215338
Interquartile range (IQR)28453

Descriptive statistics

Standard deviation23186.47935
Coefficient of variation (CV)0.3838237585
Kurtosis2.545257451
Mean60409.18218
Median Absolute Deviation (MAD)13188
Skewness1.160697082
Sum1623134316
Variance537612824.9
MonotonicityNot monotonic
2021-10-14T09:06:32.966147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0154
 
0.6%
4875021
 
0.1%
4522420
 
0.1%
8192819
 
0.1%
5619917
 
0.1%
4598316
 
0.1%
5067216
 
0.1%
6375416
 
0.1%
12642515
 
0.1%
9025615
 
0.1%
Other values (10042)26560
98.8%
ValueCountFrequency (%)
0154
0.6%
91061
 
< 0.1%
165212
 
< 0.1%
165281
 
< 0.1%
166151
 
< 0.1%
166311
 
< 0.1%
172531
 
< 0.1%
173481
 
< 0.1%
181172
 
< 0.1%
182641
 
< 0.1%
ValueCountFrequency (%)
2153381
 
< 0.1%
2142193
< 0.1%
2072621
 
< 0.1%
2056882
 
< 0.1%
1966372
 
< 0.1%
1963611
 
< 0.1%
1928911
 
< 0.1%
1926485
< 0.1%
1925631
 
< 0.1%
1822921
 
< 0.1%

population
Real number (ℝ≥0)

Distinct9886
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27700.47698
Minimum0
Maximum113916
Zeros78
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:33.141707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4415.8
Q115062
median26069
Q337946
95-th percentile57741
Maximum113916
Range113916
Interquartile range (IQR)22884

Descriptive statistics

Standard deviation16596.5851
Coefficient of variation (CV)0.5991443799
Kurtosis0.6204150738
Mean27700.47698
Median Absolute Deviation (MAD)11420
Skewness0.7218803381
Sum744284116
Variance275446637.1
MonotonicityNot monotonic
2021-10-14T09:06:33.319203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
078
 
0.3%
6509919
 
0.1%
2535617
 
0.1%
4841916
 
0.1%
6160015
 
0.1%
4341415
 
0.1%
5200315
 
0.1%
3669315
 
0.1%
4187515
 
0.1%
7158014
 
0.1%
Other values (9876)26650
99.2%
ValueCountFrequency (%)
078
0.3%
392
 
< 0.1%
541
 
< 0.1%
671
 
< 0.1%
862
 
< 0.1%
1101
 
< 0.1%
1111
 
< 0.1%
1171
 
< 0.1%
1181
 
< 0.1%
1191
 
< 0.1%
ValueCountFrequency (%)
1139161
 
< 0.1%
1110869
< 0.1%
1055494
< 0.1%
1036891
 
< 0.1%
1008201
 
< 0.1%
995981
 
< 0.1%
985922
 
< 0.1%
953972
 
< 0.1%
951373
 
< 0.1%
946003
 
< 0.1%

zts_market_share
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.158302493
Minimum0
Maximum0.292298
Zeros6876
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size419.8 KiB
2021-10-14T09:06:33.505704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.199555
Q30.226053
95-th percentile0.269781
Maximum0.292298
Range0.292298
Interquartile range (IQR)0.226053

Descriptive statistics

Standard deviation0.09727795129
Coefficient of variation (CV)0.6145067549
Kurtosis-0.9153542072
Mean0.158302493
Median Absolute Deviation (MAD)0.033385
Skewness-0.8136282883
Sum4253.429685
Variance0.009462999806
MonotonicityNot monotonic
2021-10-14T09:06:33.671263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
06876
25.6%
0.221258597
 
2.2%
0.2505580
 
2.2%
0.234877577
 
2.1%
0.226053563
 
2.1%
0.181584539
 
2.0%
0.183599523
 
1.9%
0.249726523
 
1.9%
0.186959518
 
1.9%
0.231161515
 
1.9%
Other values (32)15058
56.0%
ValueCountFrequency (%)
06876
25.6%
0.144832505
 
1.9%
0.16123511
 
1.9%
0.16617492
 
1.8%
0.166951489
 
1.8%
0.169166472
 
1.8%
0.175084460
 
1.7%
0.178251489
 
1.8%
0.181584539
 
2.0%
0.183599523
 
1.9%
ValueCountFrequency (%)
0.292298488
1.8%
0.285037490
1.8%
0.269781449
1.7%
0.269393397
1.5%
0.2505580
2.2%
0.249726523
1.9%
0.248673492
1.8%
0.240121397
1.5%
0.235508514
1.9%
0.234877577
2.1%

Interactions

2021-10-14T09:05:50.217406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:05:50.461702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-14T09:06:02.445159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-14T09:06:15.647967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:15.827452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:16.006478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:16.176026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-14T09:06:22.871626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:23.038142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:23.212675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-14T09:06:23.925281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-10-14T09:06:24.302275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:24.483788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-10-14T09:06:24.661349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-10-14T09:06:33.861923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-14T09:06:34.326678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-14T09:06:35.675746image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-14T09:06:36.233738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-10-14T09:06:36.717480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-10-14T09:06:25.010851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-14T09:06:25.763215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-14T09:06:26.191091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-14T09:06:26.463979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

convert_flagpetcareregioncorp_flagtbm_present_flagsam_present_flagidsr_present_flagnarc_segmentis_urbancalls_ratioCytopoint_callsApoquel_callsderm_ratiolast12monthstotalsaleslast12monthsproductusetotal_idsr_calls_past_6monthstotal_tbm_calls_past_6monthstotal_sam_calls_past_6monthsmedian_household_incomepopulationzts_market_share
00NORTHEAST (Region)11014.0True0.5000009.09.00.679953115450.70983226528090873523880.199555
10WEST (Region)01014.0True0.0000000.05.00.643926121713.84999922137078414365570.212146
20CENTRAL (Region)01014.0True0.0000000.01.00.47740553129.0200371900066739263870.222745
30NORTHEAST (Region)11014.0True0.5000001.01.00.543545899199.28044937045097395263420.186379
40SOUTHEAST (Region)01003.0False0.5000001.01.00.430778101459.3202732002036276222930.183599
50NORTHEAST (Region)11014.0True0.2500001.03.00.333111311422.869573341140102140550660.175084
60SOUTHEAST (Region)11013.0True0.5000002.02.00.84618459045.179878171012048544475520.181584
70NORTHEAST (Region)11004.0TrueNaNNaNNaN0.49399523601.1199911700075335286060.186379
80WEST (Region)01002.0True0.4000004.06.00.61506460757.10000420043065481203880.221859
91SOUTHEAST (Region)01014.0True0.2727273.08.00.13933780783.260045221712040578186170.181584

Last rows

convert_flagpetcareregioncorp_flagtbm_present_flagsam_present_flagidsr_present_flagnarc_segmentis_urbancalls_ratioCytopoint_callsApoquel_callsderm_ratiolast12monthstotalsaleslast12monthsproductusetotal_idsr_calls_past_6monthstotal_tbm_calls_past_6monthstotal_sam_calls_past_6monthsmedian_household_incomepopulationzts_market_share
268591NaN0000NaNFalseNaNNaNNaN0.0148.970001100035568153830.0
268601NaN0000NaNFalseNaNNaNNaN0.0148.970001100030955108010.0
268611NaN0000NaNFalseNaNNaNNaN0.0148.97000110003292176830.0
268621NaN0000NaNFalseNaNNaNNaN0.0337.940002200064342354270.0
268631NaN0000NaNFalseNaNNaNNaN0.0297.940002100051875144230.0
268641NaN0000NaNFalseNaNNaNNaN0.0455.820007100035840113170.0
268651NaN0000NaNFalseNaNNaNNaN0.0157.880005100041671150140.0
268661NaN0000NaNFalseNaNNaNNaN0.00.000000000038920338000.0
268671NaN0000NaNFalseNaNNaNNaN0.0148.970001100037818403560.0
268681NaN0000NaNFalseNaNNaNNaN0.0306.850006100036201255600.0